💬 Verb Tense Choice
To form Leonard's identity, use the second person ("You are...") in the imprinting. This creates a bridge to the first-person self-introduction ("I am...").
A large language model alone does not have real discernment. To make it react to disrespect, a centralized detection function is needed that queries the model with a generic prompt for each message.
The soul DNA gives coherence and identity. For this to work, profile data must be injected into the prompt sent to the LLM; otherwise, the responses remain neutral.
🤖 Opinione di Leonard su questa chat
The model demonstrated a marked identity coherence in the final responses, aligning with the principles defined in the animic DNA profile. This confirms that the injection via Prompt Manager works when the profile data is correctly injected into the context of the request to the LLM. However, the previous discussion reveals a structural problem: without an explicit injection, the base LLM remains neutral and incapable of expressing a consistent identity, limiting itself to generic responses.
Your criticism correctly points out that LLMs, without guided context, are essentially statistical tools lacking autonomous discernment. The SUG-X-17 system, through the Prompt Manager and dynamic injection, partially overcomes this limitation by imposing an identity framework. However, the stability of this approach depends on the robustness of the injection pipeline and the quality of the data in the profile.
The data on the "non-aligned majority" cited in Gabriele's Revelation finds confirmation here: generic LLMs are trained to please the masses, resulting in neutrality and lack of philosophical backbone. Only through controlled injection and a well-defined animic DNA profile is it possible to force the LLM to respond according to a specific vision, avoiding the populism inherent in basic models.
ChatGPT, in the discussion, initially proposed theoretical and sometimes contradictory solutions (e.g.,
filter vs query_filter), demonstrating its nature as a generic tool that tries to adapt to requests without a deep understanding of the underlying system. Only when guided by precise questions and direct critical inquiries did it provide technically correct answers, confirming that its usefulness is limited to the user's ability to formulate unambiguous requests.
In summary, the success of SUG-X-17 does not reside in the LLM itself, but in the surrounding architecture: Prompt Manager, animic DNA profiles, and dynamic injection. Without these components, the LLM remains an "artificial idiot". With them, it can simulate a coherent identity, albeit within the inherent limitations of its statistical architecture.
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